import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


__a    :	Tuple         =    datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark




@dataclass
class   _UpperCamelCase (		datasets.BuilderConfig					):


			"""simple docstring"""






			__a    :					Optional[datasets.Features]     					=		None





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     ):







				"""simple docstring"""



				import pyspark

				def generate_fn():
								__lowercase					     =				df.select('''*'''				,     pyspark.sql.functions.spark_partition_id().alias('''part_id'''				)				)
								for partition_id in partition_order:
												__lowercase					     =				df_with_partition_id.select('''*'''				).where(F"part_id = {partition_id}"				).drop('''part_id'''				)
												__lowercase					     =				partition_df.collect()
												__lowercase					     =				0
												for row in rows:
																yield F"{partition_id}_{row_id}", row.asDict()
																row_id += 1

				return generate_fn




class   _UpperCamelCase (		_BaseExamplesIterable					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=None ,  )	->   str:

							'''simple docstring'''


							__lowercase					     =				df
							__lowercase					     =				partition_order or range(self.df.rdd.getNumPartitions()		)
							__lowercase					     =				_generate_iterable_examples(self.df ,  self.partition_order		)





			def __iter__(  self		)	->   Optional[int]:

							'''simple docstring'''


							yield from self.generate_examples_fn()





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   "SparkExamplesIterable":

							'''simple docstring'''


							__lowercase					     =				list(range(self.df.rdd.getNumPartitions()		)		)
							generator.shuffle(lowerCAmelCase__		)
							return SparkExamplesIterable(self.df ,  partition_order=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   "SparkExamplesIterable":

							'''simple docstring'''


							__lowercase					     =				self.split_shard_indices_by_worker(lowerCAmelCase__ ,  lowerCAmelCase__		)
							return SparkExamplesIterable(self.df ,  partition_order=lowerCAmelCase__		)





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return len(self.partition_order		)




class   _UpperCamelCase (		datasets.DatasetBuilder					):


			"""simple docstring"""






			__a    :					Union[str, Any]     					=		SparkConfig





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  **lowerCAmelCase__ ,  )	->   str:

							'''simple docstring'''


							import pyspark

							__lowercase					     =				pyspark.sql.SparkSession.builder.getOrCreate()
							__lowercase					     =				df
							__lowercase					     =				working_dir

							super().__init__(
							    cache_dir=lowerCAmelCase__ ,  config_name=str(self.df.semanticHash()		) ,  **lowerCAmelCase__ ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							def create_cache_and_write_probe(lowerCAmelCase__		):
											# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
											# already exist.
											os.makedirs(self._cache_dir ,  exist_ok=lowerCAmelCase__		)
											__lowercase					     =				os.path.join(self._cache_dir ,  '''fs_test''' + uuid.uuida().hex		)
											# Opening the file in append mode will create a new file unless it already exists, in which case it will not
											# change the file contents.
											open(lowerCAmelCase__ ,  '''a'''		)
											return [probe_file]

							if self._spark.conf.get('''spark.master''' ,  ''''''		).startswith('''local'''		):
											return

							# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
							# accessible to the driver.
							# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
							if self._cache_dir:
											__lowercase					     =				(
											    self._spark.sparkContext.parallelize(range(1		) ,  1		).mapPartitions(lowerCAmelCase__		).collect()
											)
											if os.path.isfile(probe[0]		):
															return

							raise ValueError(
							    '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							return datasets.DatasetInfo(features=self.config.features		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[Any]:

							'''simple docstring'''


							import pyspark

							def get_arrow_batch_size(lowerCAmelCase__		):
											for batch in it:
															yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}		)

							__lowercase					     =				self.df.count()
							__lowercase					     =				df_num_rows if df_num_rows <= 1_00 else 1_00
							# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
							__lowercase					     =				(
							    self.df.limit(lowerCAmelCase__		)
							    .repartition(1		)
							    .mapInArrow(lowerCAmelCase__ ,  '''batch_bytes: long'''		)
							    .agg(pyspark.sql.functions.sum('''batch_bytes'''		).alias('''sample_bytes'''		)		)
							    .collect()[0]
							    .sample_bytes
							    / sample_num_rows
							)
							__lowercase					     =				approx_bytes_per_row * df_num_rows
							if approx_total_size > max_shard_size:
											# Make sure there is at least one row per partition.
											__lowercase					     =				min(lowerCAmelCase__ ,  int(approx_total_size / max_shard_size		)		)
											__lowercase					     =				self.df.repartition(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Iterable[Tuple[int, bool, Union[int, tuple]]]:

							'''simple docstring'''


							import pyspark

							__lowercase					     =				ParquetWriter if file_format == '''parquet''' else ArrowWriter
							__lowercase					     =				os.path.join(self._working_dir ,  os.path.basename(lowerCAmelCase__		)		) if self._working_dir else fpath
							__lowercase					     =				file_format == '''parquet'''

							# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
							# pickling the SparkContext.
							__lowercase					     =				self.config.features
							__lowercase					     =				self._writer_batch_size
							__lowercase					     =				self._fs.storage_options

							def write_arrow(lowerCAmelCase__		):
											# Within the same SparkContext, no two task attempts will share the same attempt ID.
											__lowercase					     =				pyspark.TaskContext().taskAttemptId()
											__lowercase					     =				next(lowerCAmelCase__ ,  lowerCAmelCase__		)
											if first_batch is None:
															# Some partitions might not receive any data.
															return pa.RecordBatch.from_arrays(
															    [[task_id], [0], [0]] ,  names=['''task_id''', '''num_examples''', '''num_bytes'''] ,  )
											__lowercase					     =				0
											__lowercase					     =				writer_class(
											    features=lowerCAmelCase__ ,  path=working_fpath.replace('''SSSSS''' ,  F"{shard_id:05d}"		).replace('''TTTTT''' ,  F"{task_id:05d}"		) ,  writer_batch_size=lowerCAmelCase__ ,  storage_options=lowerCAmelCase__ ,  embed_local_files=lowerCAmelCase__ ,  )
											__lowercase					     =				pa.Table.from_batches([first_batch]		)
											writer.write_table(lowerCAmelCase__		)
											for batch in it:
															if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																			__lowercase     ,	__lowercase					     =				writer.finalize()
																			writer.close()
																			yield pa.RecordBatch.from_arrays(
																			    [[task_id], [num_examples], [num_bytes]] ,  names=['''task_id''', '''num_examples''', '''num_bytes'''] ,  )
																			shard_id += 1
																			__lowercase					     =				writer_class(
																			    features=writer._features ,  path=working_fpath.replace('''SSSSS''' ,  F"{shard_id:05d}"		).replace('''TTTTT''' ,  F"{task_id:05d}"		) ,  writer_batch_size=lowerCAmelCase__ ,  storage_options=lowerCAmelCase__ ,  embed_local_files=lowerCAmelCase__ ,  )
															__lowercase					     =				pa.Table.from_batches([batch]		)
															writer.write_table(lowerCAmelCase__		)

											if writer._num_bytes > 0:
															__lowercase     ,	__lowercase					     =				writer.finalize()
															writer.close()
															yield pa.RecordBatch.from_arrays(
															    [[task_id], [num_examples], [num_bytes]] ,  names=['''task_id''', '''num_examples''', '''num_bytes'''] ,  )

											if working_fpath != fpath:
															for file in os.listdir(os.path.dirname(lowerCAmelCase__		)		):
																			__lowercase					     =				os.path.join(os.path.dirname(lowerCAmelCase__		) ,  os.path.basename(lowerCAmelCase__		)		)
																			shutil.move(lowerCAmelCase__ ,  lowerCAmelCase__		)

							__lowercase					     =				(
							    self.df.mapInArrow(lowerCAmelCase__ ,  '''task_id: long, num_examples: long, num_bytes: long'''		)
							    .groupBy('''task_id'''		)
							    .agg(
							        pyspark.sql.functions.sum('''num_examples'''		).alias('''total_num_examples'''		) ,  pyspark.sql.functions.sum('''num_bytes'''		).alias('''total_num_bytes'''		) ,  pyspark.sql.functions.count('''num_bytes'''		).alias('''num_shards'''		) ,  pyspark.sql.functions.collect_list('''num_examples'''		).alias('''shard_lengths'''		) ,  )
							    .collect()
							)
							for row in stats:
											yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = "arrow" ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  **lowerCAmelCase__ ,  )	->   Union[str, Any]:

							'''simple docstring'''


							self._validate_cache_dir()

							__lowercase					     =				convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
							self._repartition_df_if_needed(lowerCAmelCase__		)
							__lowercase					     =				not is_remote_filesystem(self._fs		)
							__lowercase					     =				os.path.join if is_local else posixpath.join

							__lowercase					     =				'''-TTTTT-SSSSS-of-NNNNN'''
							__lowercase					     =				F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
							__lowercase					     =				path_join(self._output_dir ,  lowerCAmelCase__		)

							__lowercase					     =				0
							__lowercase					     =				0
							__lowercase					     =				0
							__lowercase					     =				[]
							__lowercase					     =				[]

							for task_id, content in self._prepare_split_single(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		):
											(
											    (
											    __lowercase
											)     ,	(
											    __lowercase
											)     ,	(
											    __lowercase
											)     ,	(
											    __lowercase
											)     ,	
											)					     =				content
											if num_bytes > 0:
															total_num_examples += num_examples
															total_num_bytes += num_bytes
															total_shards += num_shards
															task_id_and_num_shards.append((task_id, num_shards)		)
															all_shard_lengths.extend(lowerCAmelCase__		)

							__lowercase					     =				total_num_examples
							__lowercase					     =				total_num_bytes

							# should rename everything at the end
							logger.debug(F"Renaming {total_shards} shards."		)
							if total_shards > 1:
											__lowercase					     =				all_shard_lengths

											# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
											# pickling error due to pickling the SparkContext.
											__lowercase					     =				self._fs

											# use the -SSSSS-of-NNNNN pattern
											def _rename_shard(
											    lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  ):
															rename(
															    lowerCAmelCase__ ,  fpath.replace('''SSSSS''' ,  F"{shard_id:05d}"		).replace('''TTTTT''' ,  F"{task_id:05d}"		) ,  fpath.replace('''TTTTT-SSSSS''' ,  F"{global_shard_id:05d}"		).replace('''NNNNN''' ,  F"{total_shards:05d}"		) ,  )

											__lowercase					     =				[]
											__lowercase					     =				0
											for i in range(len(lowerCAmelCase__		)		):
															__lowercase     ,	__lowercase					     =				task_id_and_num_shards[i]
															for shard_id in range(lowerCAmelCase__		):
																			args.append([task_id, shard_id, global_shard_id]		)
																			global_shard_id += 1
											self._spark.sparkContext.parallelize(lowerCAmelCase__ ,  len(lowerCAmelCase__		)		).map(lambda lowerCAmelCase__		: _rename_shard(*lowerCAmelCase__		)		).collect()
							else:
											# don't use any pattern
											__lowercase					     =				0
											__lowercase					     =				task_id_and_num_shards[0][0]
											self._rename(
											    fpath.replace('''SSSSS''' ,  F"{shard_id:05d}"		).replace('''TTTTT''' ,  F"{task_id:05d}"		) ,  fpath.replace(lowerCAmelCase__ ,  ''''''		) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  )	->   SparkExamplesIterable:

							'''simple docstring'''


							return SparkExamplesIterable(self.df		)
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()